NLMs: Augmenting Negation in Language Models

Abstract

Negation is the fundamental component in a natural language that reverses the semantic meaning of a sentence. It plays an extremely important role across a wide range of applications, yet they are underrepresented in pre-trained language models (LMs), resulting often in wrong inferences. In this work, we try to improve the underlying understanding of the negation in the pre-trained LMs. To augment negation understanding, we propose a language model objective with a weighted cross-entropy loss and elastic weight consolidation regularization. We reduce the mean top 1 error rate for BERT-base to 1.1\%, BERT-large to 0.78\%, RoBERTA-base to 3.74\%, RoBERTA-large to 0.01\% on the negated LAMA dataset. It minimizes the BERT error rate by a margin of 8\% and also outperform the existing negation models. We also provide empirical evidences that negated augmented models outperform the classical models on original as well as negation benchmarks on natural language inference tasks.